{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型预测&训练\n",
    "这一部分是模型的训练和预测，我们使用到的模型可以分为两类\n",
    "\n",
    "## 单模型\n",
    "### 深度学习模型\n",
    "我们联合了新闻文本和图片上的文本信息来构建模型，基本框架如下图所示\n",
    "![img](../img/example.png)\n",
    "\n",
    "### 机器学习模型\n",
    "\n",
    "* 输入特征为TFIDF+SVD、Basic Features等\n",
    "* 这里OCR出来的结果和新闻文本是做简单拼接的方式\n",
    "* 模型有：xgboost、catboost、lightGBM、DNN\n",
    "\n",
    "\n",
    "| 模型或方法          | 得分F1-measure                                   |\n",
    "| ----------- | ---------------------------------------- \n",
    "| catboost     | 0.611                                  |\n",
    "| xgboost         | 0.621                                   |\n",
    "| lightgbm      | 0.625                                   |\n",
    "| dnn         | 0.621|\n",
    "| textCNN  |0.617|\n",
    "| capsule      |0.625|\n",
    "| covlstm    |0.630|    \n",
    "| dpcnn    |0.626|    \n",
    "| lstm+gru    |0.635|    \n",
    "| lstm+gru+attention    |0.640| \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# 导入必要的包\n",
    "import pickle\n",
    "import numpy as np\n",
    "import os\n",
    "from config import Config\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.preprocessing import sequence\n",
    "from keras.utils import np_utils\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = Config()\n",
    "# 读取词对应的id\n",
    "with open(config.ITEM_TO_ID, 'rb') as f:\n",
    "    item_to_id = pickle.load(f)\n",
    "# 取最后一位作为UNK表识\n",
    "UNK = len(item_to_id)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 让我们来导入一下传统模型【xgboost、catboost、lightgbm等】的训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def static_data_prepare():\n",
    "    \"\"\"\n",
    "\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    train_y = []\n",
    "\n",
    "    with open(config.TRAIN_X, 'r', encoding='utf-8') as f:\n",
    "        lines = f.readlines()\n",
    "        for line in lines:\n",
    "            line = line.strip()\n",
    "            line = line.split('\\t')\n",
    "            label = int(line[2])\n",
    "            train_y.append(label)\n",
    "\n",
    "\n",
    "    with open(config.FEATURES_test_FILE, 'rb') as f:\n",
    "        test_features = pickle.load(f)\n",
    "\n",
    "    with open(config.FEATURES_FILE, 'rb') as f:\n",
    "        features = pickle.load(f)\n",
    "        \n",
    "    with open(config.OCR_FEATURES_test_FILE, 'rb') as f:\n",
    "        ocr_test_features = pickle.load(f)\n",
    "    with open(config.OCR_FEATURES_FILE, 'rb') as f:\n",
    "        ocr_features = pickle.load(f)\n",
    "\n",
    "    # TFIDF + SVD 降维后的特征\n",
    "    with open('../data/train_x_250.pkl', 'rb') as f:\n",
    "        train_x = pickle.load(f)\n",
    "    with open('../data/test_x_250.pkl', 'rb') as f:\n",
    "        test_x = pickle.load(f)\n",
    "\n",
    "    # 联合OCR提取的特征\n",
    "    with open('../data/ocr_train_x_250.pkl', 'rb') as f:\n",
    "        train_ocr_x = pickle.load(f)\n",
    "\n",
    "    with open('../data/ocr_test_x_250.pkl', 'rb') as f:\n",
    "        test_ocr_x = pickle.load(f)\n",
    "        \n",
    "    scaler = MinMaxScaler()\n",
    "    all_feature = np.concatenate([features, test_features, ocr_test_features, ocr_features], axis=0)\n",
    "    scaler.fit(all_feature)\n",
    "    features = scaler.transform(features)\n",
    "    test_features = scaler.transform(test_features)\n",
    "    ocr_features = scaler.transform(ocr_features)\n",
    "    ocr_test_features = scaler.transform(ocr_test_features)\n",
    "    \n",
    "    train_x = np.concatenate((train_x, train_ocr_x, features, ocr_features), axis=-1)\n",
    "    test_x = np.concatenate((test_x, test_ocr_x, test_features, ocr_test_features), axis=-1)\n",
    "    train_y = np.array(train_y)\n",
    "    return train_x, train_y, test_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "data_x, data_y, test = static_data_prepare()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "891:\tlearn: -0.0239824\ttotal: 1.99s\tremaining: 241ms\n",
      "892:\tlearn: -0.0239507\ttotal: 1.99s\tremaining: 239ms\n",
      "893:\tlearn: -0.0239175\ttotal: 1.99s\tremaining: 236ms\n",
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      "895:\tlearn: -0.0238474\ttotal: 2s\tremaining: 232ms\n",
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      "901:\tlearn: -0.0236929\ttotal: 2s\tremaining: 218ms\n",
      "902:\tlearn: -0.0236673\ttotal: 2.01s\tremaining: 216ms\n",
      "903:\tlearn: -0.0236365\ttotal: 2.01s\tremaining: 213ms\n",
      "904:\tlearn: -0.0236163\ttotal: 2.01s\tremaining: 211ms\n",
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      "909:\tlearn: -0.0234937\ttotal: 2.02s\tremaining: 200ms\n",
      "910:\tlearn: -0.0234685\ttotal: 2.02s\tremaining: 197ms\n",
      "911:\tlearn: -0.0234313\ttotal: 2.02s\tremaining: 195ms\n",
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      "914:\tlearn: -0.0233391\ttotal: 2.02s\tremaining: 188ms\n",
      "915:\tlearn: -0.0233091\ttotal: 2.03s\tremaining: 186ms\n",
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      "954:\tlearn: -0.0222560\ttotal: 2.08s\tremaining: 98.1ms\n",
      "955:\tlearn: -0.0222425\ttotal: 2.08s\tremaining: 96ms\n",
      "956:\tlearn: -0.0222090\ttotal: 2.09s\tremaining: 93.7ms\n",
      "957:\tlearn: -0.0221845\ttotal: 2.09s\tremaining: 91.5ms\n",
      "958:\tlearn: -0.0221668\ttotal: 2.09s\tremaining: 89.3ms\n",
      "959:\tlearn: -0.0221534\ttotal: 2.09s\tremaining: 87.2ms\n",
      "960:\tlearn: -0.0221401\ttotal: 2.09s\tremaining: 85ms\n",
      "961:\tlearn: -0.0221130\ttotal: 2.1s\tremaining: 82.8ms\n",
      "962:\tlearn: -0.0220954\ttotal: 2.1s\tremaining: 80.6ms\n",
      "963:\tlearn: -0.0220759\ttotal: 2.1s\tremaining: 78.4ms\n",
      "964:\tlearn: -0.0220429\ttotal: 2.1s\tremaining: 76.2ms\n",
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      "966:\tlearn: -0.0220029\ttotal: 2.1s\tremaining: 71.8ms\n",
      "967:\tlearn: -0.0219874\ttotal: 2.11s\tremaining: 69.6ms\n",
      "968:\tlearn: -0.0219635\ttotal: 2.11s\tremaining: 67.4ms\n",
      "969:\tlearn: -0.0219308\ttotal: 2.11s\tremaining: 65.2ms\n",
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      "971:\tlearn: -0.0218808\ttotal: 2.11s\tremaining: 60.8ms\n",
      "972:\tlearn: -0.0218636\ttotal: 2.11s\tremaining: 58.6ms\n",
      "973:\tlearn: -0.0218371\ttotal: 2.11s\tremaining: 56.4ms\n",
      "974:\tlearn: -0.0218135\ttotal: 2.12s\tremaining: 54.3ms\n",
      "975:\tlearn: -0.0217812\ttotal: 2.12s\tremaining: 52.1ms\n",
      "976:\tlearn: -0.0217660\ttotal: 2.12s\tremaining: 49.9ms\n",
      "977:\tlearn: -0.0217339\ttotal: 2.12s\tremaining: 47.7ms\n",
      "978:\tlearn: -0.0217188\ttotal: 2.12s\tremaining: 45.6ms\n",
      "979:\tlearn: -0.0217043\ttotal: 2.13s\tremaining: 43.4ms\n",
      "980:\tlearn: -0.0216724\ttotal: 2.13s\tremaining: 41.2ms\n",
      "981:\tlearn: -0.0216464\ttotal: 2.13s\tremaining: 39ms\n",
      "982:\tlearn: -0.0216146\ttotal: 2.13s\tremaining: 36.9ms\n",
      "983:\tlearn: -0.0215888\ttotal: 2.13s\tremaining: 34.7ms\n",
      "984:\tlearn: -0.0215572\ttotal: 2.13s\tremaining: 32.5ms\n",
      "985:\tlearn: -0.0215256\ttotal: 2.14s\tremaining: 30.3ms\n",
      "986:\tlearn: -0.0215045\ttotal: 2.14s\tremaining: 28.2ms\n",
      "987:\tlearn: -0.0214919\ttotal: 2.14s\tremaining: 26ms\n",
      "988:\tlearn: -0.0214664\ttotal: 2.14s\tremaining: 23.9ms\n",
      "989:\tlearn: -0.0214409\ttotal: 2.15s\tremaining: 21.7ms\n",
      "990:\tlearn: -0.0214097\ttotal: 2.15s\tremaining: 19.5ms\n",
      "991:\tlearn: -0.0213786\ttotal: 2.15s\tremaining: 17.3ms\n",
      "992:\tlearn: -0.0213533\ttotal: 2.15s\tremaining: 15.2ms\n",
      "993:\tlearn: -0.0213223\ttotal: 2.15s\tremaining: 13ms\n",
      "994:\tlearn: -0.0212915\ttotal: 2.15s\tremaining: 10.8ms\n",
      "995:\tlearn: -0.0212664\ttotal: 2.15s\tremaining: 8.65ms\n",
      "996:\tlearn: -0.0212440\ttotal: 2.16s\tremaining: 6.49ms\n",
      "997:\tlearn: -0.0212317\ttotal: 2.16s\tremaining: 4.33ms\n",
      "998:\tlearn: -0.0212011\ttotal: 2.16s\tremaining: 2.16ms\n",
      "999:\tlearn: -0.0211845\ttotal: 2.16s\tremaining: 0us\n",
      "Test error using softmax = 1.0\n",
      "0:\tlearn: -0.6884537\ttotal: 1.14ms\tremaining: 1.13s\n",
      "1:\tlearn: -0.6775458\ttotal: 2.71ms\tremaining: 1.35s\n",
      "2:\tlearn: -0.6668794\ttotal: 4.25ms\tremaining: 1.41s\n",
      "3:\tlearn: -0.6598844\ttotal: 6.42ms\tremaining: 1.6s\n",
      "4:\tlearn: -0.6541079\ttotal: 8.03ms\tremaining: 1.6s\n",
      "5:\tlearn: -0.6466951\ttotal: 9.14ms\tremaining: 1.51s\n",
      "6:\tlearn: -0.6400169\ttotal: 11.3ms\tremaining: 1.6s\n",
      "7:\tlearn: -0.6322848\ttotal: 13.3ms\tremaining: 1.66s\n",
      "8:\tlearn: -0.6258071\ttotal: 15.5ms\tremaining: 1.7s\n",
      "9:\tlearn: -0.6199390\ttotal: 17.5ms\tremaining: 1.73s\n",
      "10:\tlearn: -0.6136578\ttotal: 19.6ms\tremaining: 1.76s\n",
      "11:\tlearn: -0.6044708\ttotal: 21.1ms\tremaining: 1.74s\n",
      "12:\tlearn: -0.5967205\ttotal: 22.3ms\tremaining: 1.69s\n",
      "13:\tlearn: -0.5897347\ttotal: 23.8ms\tremaining: 1.68s\n",
      "14:\tlearn: -0.5828669\ttotal: 25.4ms\tremaining: 1.67s\n",
      "15:\tlearn: -0.5725592\ttotal: 26.9ms\tremaining: 1.66s\n",
      "16:\tlearn: -0.5659848\ttotal: 28.4ms\tremaining: 1.64s\n",
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      "25:\tlearn: -0.5081683\ttotal: 47.9ms\tremaining: 1.79s\n",
      "26:\tlearn: -0.5036837\ttotal: 49.5ms\tremaining: 1.78s\n",
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      "36:\tlearn: -0.4535198\ttotal: 74.2ms\tremaining: 1.93s\n",
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      "38:\tlearn: -0.4423783\ttotal: 78.5ms\tremaining: 1.93s\n",
      "39:\tlearn: -0.4374968\ttotal: 80.3ms\tremaining: 1.93s\n",
      "40:\tlearn: -0.4309044\ttotal: 81.7ms\tremaining: 1.91s\n",
      "41:\tlearn: -0.4266852\ttotal: 83.8ms\tremaining: 1.91s\n",
      "42:\tlearn: -0.4239944\ttotal: 87ms\tremaining: 1.94s\n",
      "43:\tlearn: -0.4188254\ttotal: 88.5ms\tremaining: 1.92s\n",
      "44:\tlearn: -0.4153470\ttotal: 91ms\tremaining: 1.93s\n",
      "45:\tlearn: -0.4096194\ttotal: 92.5ms\tremaining: 1.92s\n",
      "46:\tlearn: -0.4039400\ttotal: 93.6ms\tremaining: 1.9s\n",
      "47:\tlearn: -0.3974244\ttotal: 94.8ms\tremaining: 1.88s\n",
      "48:\tlearn: -0.3942610\ttotal: 96.4ms\tremaining: 1.87s\n",
      "49:\tlearn: -0.3886822\ttotal: 97.5ms\tremaining: 1.85s\n",
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      "54:\tlearn: -0.3644923\ttotal: 104ms\tremaining: 1.79s\n",
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      "59:\tlearn: -0.3459273\ttotal: 112ms\tremaining: 1.76s\n",
      "60:\tlearn: -0.3433709\ttotal: 114ms\tremaining: 1.75s\n",
      "61:\tlearn: -0.3389330\ttotal: 115ms\tremaining: 1.74s\n",
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      "64:\tlearn: -0.3299243\ttotal: 119ms\tremaining: 1.71s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65:\tlearn: -0.3271757\ttotal: 122ms\tremaining: 1.73s\n",
      "66:\tlearn: -0.3225274\ttotal: 124ms\tremaining: 1.73s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "990:\tlearn: -0.0206252\ttotal: 1.58s\tremaining: 14.3ms\n",
      "991:\tlearn: -0.0206055\ttotal: 1.58s\tremaining: 12.8ms\n",
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      "999:\tlearn: -0.0204295\ttotal: 1.6s\tremaining: 0us\n",
      "Test error using softmax = 1.0\n",
      "0:\tlearn: -0.6798083\ttotal: 1.13ms\tremaining: 1.13s\n",
      "1:\tlearn: -0.6713676\ttotal: 2.72ms\tremaining: 1.35s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "999:\tlearn: -0.0209135\ttotal: 1.88s\tremaining: 0us\n",
      "Test error using softmax = 0.5\n",
      "0:\tlearn: -0.6877494\ttotal: 1.5ms\tremaining: 1.5s\n",
      "1:\tlearn: -0.6833684\ttotal: 4.54ms\tremaining: 2.27s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "999:\tlearn: -0.0177490\ttotal: 1.7s\tremaining: 0us\n",
      "Test error using softmax = 0.5\n",
      "0:\tlearn: -0.6789775\ttotal: 1.84ms\tremaining: 1.84s\n",
      "1:\tlearn: -0.6723595\ttotal: 4.09ms\tremaining: 2.04s\n",
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      "987:\tlearn: -0.0167892\ttotal: 1.9s\tremaining: 23.1ms\n",
      "988:\tlearn: -0.0167755\ttotal: 1.9s\tremaining: 21.1ms\n",
      "989:\tlearn: -0.0167609\ttotal: 1.9s\tremaining: 19.2ms\n",
      "990:\tlearn: -0.0167418\ttotal: 1.9s\tremaining: 17.3ms\n",
      "991:\tlearn: -0.0167255\ttotal: 1.91s\tremaining: 15.4ms\n",
      "992:\tlearn: -0.0167064\ttotal: 1.91s\tremaining: 13.5ms\n",
      "993:\tlearn: -0.0166903\ttotal: 1.91s\tremaining: 11.5ms\n",
      "994:\tlearn: -0.0166713\ttotal: 1.91s\tremaining: 9.61ms\n",
      "995:\tlearn: -0.0166578\ttotal: 1.91s\tremaining: 7.68ms\n",
      "996:\tlearn: -0.0166417\ttotal: 1.91s\tremaining: 5.76ms\n",
      "997:\tlearn: -0.0166257\ttotal: 1.92s\tremaining: 3.84ms\n",
      "998:\tlearn: -0.0166096\ttotal: 1.92s\tremaining: 1.92ms\n",
      "999:\tlearn: -0.0165908\ttotal: 1.92s\tremaining: 0us\n",
      "Test error using softmax = 1.0\n",
      "total scores is  0.8\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "# 这里先导入传统机器学习模型\n",
    "model = config.model['model' + str(8)]()\n",
    "model.train_predict(data_x[:10], data_y[:10], test[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 让我们来导入一下深度学习模型的训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deep_data_prepare():\n",
    "    \"\"\"\n",
    "\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    train_x = []\n",
    "    train_y = []\n",
    "    ocr_train = []\n",
    "    ocr_test = []\n",
    "    test_x = []\n",
    "\n",
    "    STOP_WORD = set()\n",
    "\n",
    "\n",
    "    # 加载停用词\n",
    "    with open(config.STOP_WORD_FILE, 'r', encoding='utf-8') as f:\n",
    "        lines = f.readlines()\n",
    "        for line in lines:\n",
    "            line = line.strip()\n",
    "            STOP_WORD.add(line)\n",
    "\n",
    "\n",
    "    test_ids = []\n",
    "    with open(config.TEST_FILE, 'r', encoding='utf-8') as f:\n",
    "        lines = f.readlines()\n",
    "        for line in lines:\n",
    "            line = line.strip().split('\\t')\n",
    "            test_ids.append(line[0])\n",
    "            line = line[1].split(' ')\n",
    "            tmp = []\n",
    "            for l in line:\n",
    "                if l == '' \\\n",
    "                        or l == ' ' \\\n",
    "                        or l in STOP_WORD\\\n",
    "                        or len(l) < 2:\n",
    "                    continue\n",
    "\n",
    "\n",
    "                if l in item_to_id:\n",
    "                    id = item_to_id[l]\n",
    "                    tmp.append(id)\n",
    "\n",
    "            if len(tmp) == 0:\n",
    "                tmp = [UNK]\n",
    "\n",
    "            # 对于超过最大长度的我们只取前面的\n",
    "            if len(tmp) > config.MAX_LEN:\n",
    "                # print('Length Exceed!')\n",
    "                tmp = tmp[:config.MAX_LEN]\n",
    "\n",
    "            test_x.append(tmp)\n",
    "\n",
    "\n",
    "    with open(config.TRAIN_X, 'r', encoding='utf-8') as f:\n",
    "        lines = f.readlines()\n",
    "        for line in lines:\n",
    "            line = line.strip()\n",
    "            line = line.split('\\t')\n",
    "            label = line[2]\n",
    "            line = line[1].split(' ')\n",
    "            tmp = []\n",
    "            for l in line:\n",
    "                if l == '' \\\n",
    "                        or l == ' ' \\\n",
    "                        or l in STOP_WORD\\\n",
    "                        or len(l) < 2:\n",
    "                    continue\n",
    "\n",
    "\n",
    "                if l in item_to_id:\n",
    "                    id = item_to_id[l]\n",
    "                    tmp.append(id)\n",
    "\n",
    "            if len(tmp) == 0:\n",
    "                tmp = [UNK]\n",
    "\n",
    "            # 对于超过最大长度的我们只取前面的\n",
    "            if len(tmp) > config.MAX_LEN:\n",
    "                # print('Length Exceed!')\n",
    "                tmp = tmp[:config.MAX_LEN]\n",
    "            train_x.append(tmp)\n",
    "            train_y.append(label)\n",
    "\n",
    "            \n",
    "    # 加载ocr模型数据\n",
    "    \n",
    "    with open(config.OCR_TRAIN_X, 'r', encoding='utf-8') as f:\n",
    "        lines = f.readlines()\n",
    "        for line in lines:\n",
    "            line = line.strip()\n",
    "            line = line.split('\\t')\n",
    "            line = line[1].split(' ')\n",
    "            tmp = []\n",
    "            for l in line:\n",
    "                if l == '' \\\n",
    "                        or l == ' ' \\\n",
    "                        or l in STOP_WORD\\\n",
    "                        or len(l) < 2:\n",
    "                    continue\n",
    "\n",
    "\n",
    "                if l in item_to_id:\n",
    "                    id = item_to_id[l]\n",
    "                    tmp.append(id)\n",
    "\n",
    "            if len(tmp) == 0:\n",
    "                tmp = [UNK]\n",
    "\n",
    "            # 对于超过最大长度的我们只取前面的\n",
    "            if len(tmp) > config.MAX_LEN:\n",
    "                # print('Length Exceed!')\n",
    "                tmp = tmp[:config.MAX_LEN]\n",
    "            ocr_train.append(tmp)\n",
    "            \n",
    "    with open(config.OCR_TEST_X, 'r', encoding='utf-8') as f:\n",
    "        lines = f.readlines()\n",
    "        for line in lines:\n",
    "            line = line.strip().split('\\t')\n",
    "            test_ids.append(line[0])\n",
    "            line = line[1].split(' ')\n",
    "            tmp = []\n",
    "            for l in line:\n",
    "                if l == '' \\\n",
    "                        or l == ' ' \\\n",
    "                        or l in STOP_WORD\\\n",
    "                        or len(l) < 2:\n",
    "                    continue\n",
    "\n",
    "\n",
    "                if l in item_to_id:\n",
    "                    id = item_to_id[l]\n",
    "                    tmp.append(id)\n",
    "\n",
    "            if len(tmp) == 0:\n",
    "                tmp = [UNK]\n",
    "\n",
    "            # 对于超过最大长度的我们只取前面的\n",
    "            if len(tmp) > config.MAX_LEN:\n",
    "                # print('Length Exceed!')\n",
    "                tmp = tmp[:config.MAX_LEN]\n",
    "\n",
    "            ocr_test.append(tmp)\n",
    "\n",
    "\n",
    "    train_x = sequence.pad_sequences(train_x, maxlen=config.MAX_LEN, padding='post',\n",
    "                                                         truncating='post', value=UNK)\n",
    "\n",
    "    ocr_train = sequence.pad_sequences(ocr_train, maxlen=config.OCR_LEN, padding='post',\n",
    "                                                         truncating='post', value=UNK)\n",
    "    \n",
    "    test_x = sequence.pad_sequences(test_x, maxlen=config.MAX_LEN, padding='post',\n",
    "                                                         truncating='post', value=UNK)\n",
    "\n",
    "    ocr_test = sequence.pad_sequences(ocr_test, maxlen=config.OCR_LEN, padding='post',\n",
    "                                                         truncating='post', value=UNK)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    train_y = np_utils.to_categorical(train_y)\n",
    "    print('train_x shape is: ', train_x.shape)\n",
    "    print('train_y shape is: ', train_y.shape)\n",
    "\n",
    "    test = {}\n",
    "    test['news'] = test_x\n",
    "    test['ocr'] = ocr_test\n",
    "\n",
    "    train = {}\n",
    "    train['news'] = train_x\n",
    "    train['ocr'] = ocr_train\n",
    "    return train, train_y, test\n",
    "\n",
    "def init_embedding():\n",
    "    print('vocabulary size : ', len(item_to_id) + 1)\n",
    "    print('begin to load word2vec file')\n",
    "    def get_coefs(word, *arr): return word, np.asarray(arr, dtype='float32')\n",
    "    embeddings_index = dict(get_coefs(*o.rstrip().rsplit(' ')) for o in open(config.EMBEDDING_FILE))\n",
    "\n",
    "    all_embs = np.stack(embeddings_index.values())\n",
    "    emb_mean, emb_std = all_embs.mean(), all_embs.std()\n",
    "\n",
    "    print('create embedding matrix')\n",
    "    embedding_matrix = np.random.normal(emb_mean, emb_std, (len(item_to_id) + 1, config.EMBED_SIZES))\n",
    "    for word, i in item_to_id.items():\n",
    "        embedding_vector = embeddings_index.get(word)\n",
    "        if embedding_vector is not None: embedding_matrix[i] = embedding_vector\n",
    "    print('end load word2vec')\n",
    "    return embedding_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_x shape is:  (48480, 1000)\n",
      "train_y shape is:  (48480, 3)\n",
      "vocabulary size :  153325\n",
      "begin to load word2vec file\n",
      "create embedding matrix\n",
      "end load word2vec\n"
     ]
    }
   ],
   "source": [
    "data_x, data_y, test = deep_data_prepare()\n",
    "init_emb = init_embedding()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 这里是导入深度学习模型，估计使用的模型可以参考config.py文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = config.model['model' + str(1)](config.MAX_LEN, config.OCR_LEN, (len(item_to_id) + 1), init_emb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "news (InputLayer)               (None, 1000)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "ocr (InputLayer)                (None, 400)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           multiple             45997500    news[0][0]                       \n",
      "                                                                 ocr[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "spatial_dropout1d_1 (SpatialDro (None, 1000, 300)    0           embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "spatial_dropout1d_2 (SpatialDro (None, 400, 300)     0           embedding[1][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_1 (Bidirectional) (None, 1000, 120)    129960      spatial_dropout1d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_3 (Bidirectional) (None, 400, 60)      59580       spatial_dropout1d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_2 (Bidirectional) (None, 1000, 120)    65160       bidirectional_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_4 (Bidirectional) (None, 400, 60)      16380       bidirectional_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "last (Lambda)                   (None, 120)          0           bidirectional_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_1 (GlobalM (None, 120)          0           bidirectional_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "attention_weighted_average_1 (A (None, 120)          120         bidirectional_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d_1 (Glo (None, 120)          0           bidirectional_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_2 (GlobalM (None, 60)           0           bidirectional_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "attention_weighted_average_2 (A (None, 60)           60          bidirectional_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 600)          0           last[0][0]                       \n",
      "                                                                 global_max_pooling1d_1[0][0]     \n",
      "                                                                 attention_weighted_average_1[0][0\n",
      "                                                                 global_average_pooling1d_1[0][0] \n",
      "                                                                 global_max_pooling1d_2[0][0]     \n",
      "                                                                 attention_weighted_average_2[0][0\n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 600)          0           concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 3)            1803        dropout_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 46,270,563\n",
      "Trainable params: 273,063\n",
      "Non-trainable params: 45,997,500\n",
      "__________________________________________________________________________________________________\n",
      "Train on 36360 samples, validate on 12120 samples\n",
      "Epoch 1/6\n",
      "  192/36360 [..............................] - ETA: 50:09 - loss: 1.0754 - acc: 0.4427"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-9cf9a27c7039>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Desktop/SOHU_competition/src/model/model_basic.py\u001b[0m in \u001b[0;36mtrain_predict\u001b[0;34m(self, train, train_y, test, option, true_length)\u001b[0m\n\u001b[1;32m    118\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m                 self.model.fit(kfold_X_train, y_train, batch_size=self.batch_size, epochs=6, verbose=1,\n\u001b[0;32m--> 120\u001b[0;31m                                validation_data=(kfold_X_valid, y_test))\n\u001b[0m\u001b[1;32m    121\u001b[0m                 \u001b[0madam_optimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclipvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1667\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1668\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1669\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1671\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m   1204\u001b[0m                         \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1206\u001b[0;31m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1207\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1208\u001b[0m                         \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2473\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2474\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[0;32m-> 2475\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2476\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    893\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    896\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1126\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1127\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1128\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1129\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1130\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1342\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1343\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1344\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1345\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1346\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1348\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1351\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1327\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[1;32m   1328\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1329\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1331\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "model.train_predict(data_x, data_y, test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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